It is very important in quality inspection of digital images to determine whether or not there is a defective area where pixel values do not vary evenly. Especially in recent years, image sensor devices such as digital still cameras and mobiles phones with built-in cameras have been increasingly demanded. This has increased demands for image sensor devices of higher quality. Thus, it has been increasingly demanded to detect defective areas that occur in digital images (image-sensor image) taken by use of the image sensor devices, including spot defects, unevenness defects, line defects and the like, during inspection.
In other words, even if the image sensor devices are non-defective devices, image values do not become constant owning to noise components and components called shading, in which pixels values change gently with respect to pixel coordinates. Especially in image-sensor images, presence of spot defects, unevenness defects, line defects and the like cause the pixels values to change in a complicated manner according to the respective defects with respect to the pixel coordinates. It is therefore not easy to detect the defects. Thus, a method of detecting image quality with high sensitivity has been demanded.
The spot defect indicates a state in which plural pixel values of an area each differ from the surrounding pixel values by a difference smaller than that in the point defect. The point defect indicates a state in which a single pixel value of the image-sensor image differs significantly from respective image values of the surrounding eight pixels and is thus an outstanding value (or a depressed value). The unevenness defect indicates a state in which plural pixels having pixel values that differ from one another by a smaller difference than that in the pixels of the spot defects. The line defect indicates a state in which pixel values in a row direction of the image-sensor image, pixel values in a column direction of the image-sensor image, or pixel values in a slanting direction at an arbitrary angle are significantly different from the surrounding pixel values and are thus outstanding values (or depressed values).
The shading indicates a state in which the pixel value changes gently with respect to the pixel coordinate and decreases toward an upper end, a lower end, a rightward end, and a leftward end of the image-sensor image. The shading occurs owning to reduction in sensitivity of a pixel at an edge of the image with respect to a pixel at the center of the image.
Conventionally, spot defects, unevenness defects, line defects and the like on flat panel displays, such as image-sensor images and liquid crystal panels, have been visually inspected by inspecting staff. This way of inspection depends on subjective judgment of the inspecting staff and therefore has a problem that the results of this inspection are not consistent because standards of the inspection vary among the inspecting staff or because of physical condition of the inspecting staff at the time of the inspection. There is another problem that it is difficult with this way of inspection to quantify the defects. Thus, inspecting devices that quantify the defects and detect the defects during production of the image sensor devices have been developed in recent years, and automation with the inspecting devices has also been carried out. The inspecting device detects the defects generally by actually taking an image and carrying out image processing on the image thus taken.
For example “Japanese Unexamined Patent Publication No. 2004-294202 (published on Oct. 21, 2004)” (hereinafter, “Publication 1”) discloses the defect detecting method described below. In this method, first of all, reduced images of image values of images (detected image) taken by an image pickup device are created in several sizes according to the types of the defects, and filtering is carried out on the image values to emphasize the defects. Then, statistical processing of information on luminance in the detected image is carried out on bright defects and on dark defects separately. Thereafter, a threshold value for detecting potential defects is determined on the basis of the statistical data. Then, potential defects are detected. Thereafter, evaluation values of the potential defects thus detected are obtained quantitatively. The foregoing makes it possible to determine, according to the kinds of defects, whether or not the image detected contains a potential defect.
Concretely, in spot-defect detection carried out in the defect detecting method, a process with application of a smoothing filter or a morphological operation (smoothing process) is first carried out. Then, an image obtained as a result of the smoothing process is reduced to various sizes to create reduced images. Thereafter, contrast of the spot defects in the reduced images are emphasized with the use of a top-hat filter that is a spatial filter. At this time, offset processing is also carried out to allow the process of emphasizing dark points to be carried out. Then, statistical computation is carried out on the basis of the luminance values of respective pixels of the detected image, and the threshold value is determined by use of statistical data obtained as a result of the statistical computation. On the basis of the threshold value thus determined for luminance, it is determined whether or not the detected image contains a potential defect.
In Publication 1, the smoothing process is always carried out during detection of spot defects, streak defects, unevenness defects, and line defects, in order to eliminate noise components. However, with the smoothing process only, the impact of the noise components is still high. Thus, in the defect detecting method of Publication 1, there is a possibility of erroneous determination as to whether a potential defect is present or not.
For example in the case in which the statistical processing of Publication 1 is carried out on the image having undergone edge detection as shown in FIG. 24, a defective area A (see FIG. 24), which is desired to be detected, is buried in the noise components as shown in FIG. 25. Thus, with the defect detecting method of Publication 1, there is a possibility of erroneous determination indicating that there is no potential defect is made even though there is a defect that is supposed to be determined as a potential defect.
Further, Publication 1 uses, as an image that is to be detected, a luminance image of the combination of RGB. Thus, with the defect detecting method of Publication 1, there is a possibility of erroneous determination as to whether a potential defect is present or not, in the case of image sensor devices in which defects occur by the combination of RG or GB.
For example, the image shown in FIG. 26 is an image obtained as a result of below-described edge detection carried out on the luminance image. FIG. 27 shows that carrying out the statistical processing on the image having undergone the edge detection lowers sensitivity in detection of line defects that are desired to be detect, and therefore the defect that is not supposed to be detected as the line defect is emphasized. Thus, this case also has a possibility that a line defect that is supposed to be detected is not detectable by the defect detecting method of Publication 1.